The present invention generally relates to moving targets, and more particularly to a system and method for automatic recognition of formations in moving target indication data, particularly for ground moving targets.
Although airborne sensors of ground moving targets (GMTIs) have been in use for years, the development of moving target exploitation (MTE) tools has been of interest only recently. A military formation is defined as a collection of vehicles traveling together that has a distinguishing shape that is related to its function. A formation of vehicles are in spatial proximity to each other, traveling at approximately the same velocity and arranged in an approximation of some “ideal” geometry.
The recognition of formations is indicative of the intentions and perceptions of ground forces. For example, a wedge formation is used when aggressively moving towards contact with an enemy. A wedge allows firepower to be projected forward while offering protection on the sides. A column formation, such as a convoy is much more vulnerable to ambush and would usually be seen where there is (perceived) safety, far behind the battle line.
Hostile ground-troop formations are sometimes laid out according to a definite geometry which indicates something about the specific purpose of the ground troops, the concerns of the commander, and the element of danger that these ground troops represent to our own troops. Formation-geometries and their purposes may be defined by written doctrine for some armies, for others they may be known through past observation.
Modern sensor platforms can detect and record moving targets over a large surveillance area. Displays of moving targets appear as hundreds of unidentified points, most re-appearing in a slightly different location with each radar scan. It can be difficult for image analysts to identify formations in such a dynamic display, especially if the formations are small.
Stroud and Gordon of Los Alamos National Laboratory describe a cognitive architecture for recognizing formations in order to simulate the function of an image analyst looking at MTI data in battle simulations in “Automated military unit identification in battlefield simulation”, Proceedings of the SPIE, Vol. 3069, pp. 375-386, 1997. The cognitive architecture has two parts: “an autonomous behavior which performs low-level cognition; and a higher level mechanism that can adapt the low-level behavior to changing environmental conditions.” The low level cognition is actually a bank of 2-D cluster filters whose behavior is determined by a control-parameter set containing seven elements: (1) minimum filter scale size; (2) maximum filter scale size; (3) maximum battalion area; (4) ellipse crisping factor; (5) minimum number of blips to qualify as a battalion; (6) maximum number of blips to qualify as a battalion; and (7) maximum number of steps per cluster search. These filters are designed to be rotationally invariant and respond to geometric characteristics, such as ellipicity and triangularity, which allows the filter parameters to be utilized to infer formation shape. The parameters of this set of filters are determined autonomously by the higher level cognitive part of the algorithm. High level cognition is a genetic algorithm that subjects the control-parameter set to an evolutionary process by evaluation of its performance on multiple random data sets that are consistent with the observed conditions in the region under surveillance. The outputs of the entire process are groups of MTIs and their individual 2-D cluster-filter control-parameter sets.
This methodology automatically finds clusters over a large dynamic range of sizes and, in the process, characterizes them. However, the emphasis is on clustering (e.g., battalions) not on recognition. The characterization of formation shape is limited to what may be inferred from responses of the individual filters. Additionally, range-rate data is not utilized.
Thus, a need exists for the ability to identify recognized formation-types (e.g. wedge, column, etc.), their locations and headings, and their internal configurations (e.g. size) that can be utilized for interpretation and planning.